Combining desorption electrospray ionization mass spectrometry and nuclear magnetic resonance for differential metabolomics without sample preparation

Desorption electrospray ionization mass spectrometry (DESI-MS) and nuclear magnetic resonance (NMR) spectroscopy are used to provide data on urine examined without sample preparation to allow differentiation between diseased (lung cancer) and healthy mice. Principal component analysis (PCA) is used to shortlist compounds with potential for biomarker screening which are responsible for significant differences between control urine samples and samples from diseased animals. Similar PCA score plots have been achieved by DESI-MS and NMR, using a subset of common detected metabolites. The common compounds detected by DESI and NMR have the same changes in sign of their concentrations thereby indicating the usefulness of corroborative analytical methods. The effects of different solvents and surfaces on the DESI mass spectra are also evaluated and optimized. Over 80 different metabolites were successfully identified by DESI-MS and tandem mass spectrometry experiments, with no prior sample preparation.     

Electrospray ionization mass spectrometry and partial least squares discriminant analysis applied to the quality control of olive oil

Direct infusion electrospray ionization mass spectrometry in the positive ion mode [ESI(+)‐MS] is used to obtain fingerprints of aqueous–methanolic extracts of two types of olive oils, extra virgin (EV) and ordinary (OR), as well as of samples of EV olive oil adulterated by the addition of OR olive oil and other edible oils: corn (CO), sunflower (SF), soybean (SO) and canola (CA). The MS data is treated by the partial least squares discriminant analysis (PLS‐DA) protocol aiming at discriminating the above‐mentioned classes formed by the genuine olive oils, EV (1) and OR (2), as well as the EV adulterated samples, i.e. EV/SO (3), EV/CO (4), EV/SF (5), EV/CA (6) and EV/OR (7). The PLS‐DA model employed is built with 190 and 70 samples for the training and test sets, respectively. For all classes (1–7), EV and OR olive oils as well as the adulterated samples (in a proportion varying from 0.5 to 20.0% w/w) are properly classified. The developed methodology required no ions identification and demonstrated to be fast, as each measurement lasted about 3 min including the extraction step and MS analysis, and reliable, because high sensitivities (rate of true positives) and specificities (rate of true negatives) were achieved. Finally, it can be envisaged that this approach has potential to be applied in quality control of EV olive oils. Copyright © 2013 John Wiley & Sons, Ltd.     

Theory of error for target factor analysis with applications to mass spectrometry and nuclear magnetic resonance spectrometry

Based on the theory of error for abstract factor analysis described earlier, a theory of error for target factor analysis is developed. The theory shows how the error in the data matrix mixes with the error in the target test vector. The apparent error in a target test is found to be a vector sum of the real error in the target vector and the real error in the predicted vector. The theory predicts the magnitudes of these errors without requiring any a priori knowledge of the error in the data matrix or the target vector. A reliability function and a spoil function are developed for the purpose of assessing the validity and the worthi-ness of a target vector. Examples from model data, mass spectrometry and nuclear magnetic -resonance spectrometry are presented.     

Coal analysis by application of the partial least squares method to infrared spectra

Summary A method for the rapid characterization of brown coals based on the application of a powerful method of principal component analysis, the Partial Least Squares method (PLS), is described. A data catalogue of 44 brown coals from different deposits of East European and Asian countries was used, which lists analyses of several coal parameters. Infrared spectra of brown coals were recorded and different coal properties were predicted with the help of PLS. The results show that, with the PLS-method used, several coal properties can be predicted and it is possible to describe the classification of a coal for several technological processes. The developed method is useful because of the multiplicity of information obtained within a short time and because it is possible to replace lengthy classical methods.     

Characterization of the aromatic composition of some liquid foods by nuclear magnetic resonance spectrometry and liquid chromatography with nuclear magnetic resonance and mass spectrometric detection

This paper describes the first application of NMR spectroscopy and LC-NMR/MS to the direct analysis of the aromatic composition of beer, grape juice and a wine phenolic extract. NMR spectroscopy provides non-invasive information on the overall aromatic profile and enables the identification of some compounds. However, a more comprehensive assignment is hindered by the low peak intensity and strong signal overlap in the low-field spectral region, as well as by the inherent lack of scalar coupling information for many aromatic compounds present. LC-NMR/MS can overcome these problems and is shown to aid significantly in the identification of aromatic compounds composing all samples analyzed. Some examples are the identification of several cinnamic acids (e.g. p-coumaric, trans-coutaric and trans-caftaric) in grape juice, the identification of 2-phenylethanol, tyrosol and tryptophol in beer and the detection of phenolics such as catechin, epicatechin, trans-resveratrol, tyrosol and caffeic acid in the wine extract.     

Near-infrared imaging for quantitative analysis of active component in counterfeit dimethomorph using partial least squares regression

Near-infrared (NIR) imaging systems simultaneously record spectral and spatial information. Near-infrared imaging was applied to the identification of (E,Z)-4-(3-(4-chlorophenyl)-3-(3,4-dimethoxyphenyl)acryloyl)morpholine (dimethomorph) in both mixed samples and commercial formulation in this study. The distributions of technical dimethomorph and additive in the heterogeneous counterfeit product were obtained by the relationship imaging (RI) mode. Furthermore, a series of samples which consisted of different contents of uniformly distributed dimethomorph were prepared and three data cubes were generated for each content. The spectra extracted from these images were imported to establish the partial least squares model. The model??s evaluating indicators were: coefficient of determination (R ²) 99.42 %, root mean square error of calibration (RMSEC) 0.02612, root mean square error of cross-validation (RMSECV) 0.01693, RMSECV_mean 0.03577, relative standard error of prediction (RSEP) 0.01999, and residual predictive deviation (RPD) 15.14. Relative error of prediction of the commercial formulation was 0.077, indicating the predicted value correlated with the real content. The chemical value reconstruction image of dimethomorph formulation products was calculated by a MATLAB program. NIR microscopy imaging here manifests its potential in identifying the active component in the counterfeit pesticide and quantifying the active component in its scanned image.     

Multiple component isolation in preparative multidimensional gas chromatography with characterisation by mass spectrometry and nuclear magnetic resonance spectroscopy

The preparative scale isolation of multiple components from an essential oil matrix is described using multidimensional gas chromatography (prep-MDGC) which allows their further characterisation by mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy. Menthol, linalyl acetate, carvone and geraniol were isolated individually, and were also collected in various combinations. It was demonstrated to be possible to collect multiple selected components from numerous repeat injections of the sample, to permit increased mass recovery from an external cryotrap collection device. Peak retention times remained reproducible (<0.3 s) over the repeated injections and switching events. This methodology may be utilised to confirm peak identity or to produce unique mixed-component reference standards, for instance to allow their identification in other samples using GC/MS, or identify them in comprehensive two-dimensional gas chromatography (GC × GC) analysis.     

Partial least‐squares discriminant analysis optimized by particle swarm optimization: application to 1H nuclear magnetic resonance analysis of lung cancer metabonomics

The complexity of metabolic profiles makes chemometric tools indispensable for extracting the most significant information. Partial least‐squares discriminant analysis (PLS‐DA) acts as one of the most effective strategies for data analysis in metabonomics. However, its actual efficacy in metabonomics is often weakened by the high similarity of metabolic profiles, which contain excessive variables. To rectify this situation, particle swarm optimization (PSO) was introduced to improve PLS‐DA by simultaneously selecting the optimal sample and variable subsets, the appropriate variable weights, and the best number of latent variables (SVWL) in PLS‐DA, forming a new algorithm named PSO‐SVWL‐PLSDA. Combined with ¹H nuclear magnetic resonance‐based metabonomics, PSO‐SVWL‐PLSDA was applied to recognize the patients with lung cancer from the healthy controls. PLS‐DA was also investigated as a comparison. Relatively to the recognition rates of 86% and 65%, which were yielded by PLS‐DA, respectively, for the training and test sets, those of 98.3% and 90% were offered by PSO‐SVWL‐PLSDA. Moreover, several most discriminative metabolites were identified by PSO‐SVWL‐PLSDA to aid the diagnosis of lung cancer, including lactate, glucose (α‐glucose and β‐glucose), threonine, valine, taurine, trimethylamine, glutamine, glycoprotein, proline, and lipid. Copyright © 2015 John Wiley & Sons, Ltd.     

Semi‐supervised kernel partial least squares fault detection and identification approach with application to HGPWLTP

In this paper, fault detection and identification methods based on semi‐supervised Laplacian regularization kernel partial least squares (LRKPLS) are proposed. In Laplacian regularization learning framework, unlabeled and labeled samples are used to improve estimate of data manifold so that one can establish a more robust data model. We show that LRKPLS can avoid the over‐fitting problem which may be caused by sample insufficient and outliers present. Moreover, the proposed LRKPLS approach has no special restriction on data distribution, in other words, it can be used in the case of nonlinear or non‐Gaussian data. On the basis of LRKPLS, corresponding fault detection and identification methods are proposed. Those methods are used to monitor a numerical example and Hot Galvanizing Pickling Waste Liquor Treatment Process (HGPWLTP), and the cases study show effeteness of the proposed approaches. Copyright © 2016 John Wiley & Sons, Ltd.     

Sample-distance partial least squares: PLS optimized for many variables,with application to CoMFA

Summary Three-dimensional molecular modeling can provide an unlimited number m of structural properties. Comparative Molecular Field Analysis (CoMFA), for example, may calculate thousands of field values for each model structure. When m is large, partial least squares (PLS) is the statistical method of choice for fitting and predicting biological responses. Yet PLS is usually implemented in a property-based fashion which is optimal only for small m. We describe here a sample-based formulation of PLS which can be used to fit any single response (bioactivity). SAMPLS reduces all explanatory data to the pairwise distances among n sample (molecules), or equivalently to an n-by-n covariance matrix C. This matrix, unmodified, can be used to fit all PLS components. Furthermore, SAMPLS will validate the model by modern resampling techniques, at a cost independent of m. We have implemented SAMPLS as a Fortran program and have reproduced conventional and cross-validated PLS analyses of data from two published studies. Full (leaveach-out) cross-validation of a typical CoMFA takes 0.2 CPU s. SAMPLS is thus ideally suited to structure-activity analysis based on CoMFA fields or bonded topology. The sample-distance formulation also relates PLS to methods like cluster analysis and nonlinear mapping, and shows how drastically PLS simplifies the information in CoMFA fields.Abbreviations PLS partial least squares - SAMPLS sample-distance partial least squares - CoMFA comparative molecular field analysis.     

Structural characterization of new defective molecules in poly(amidoamide) dendrimers by combining mass spectrometry and nuclear magnetic resonance

A new side-reaction occurring during divergent synthesis of PAMAM dendrimers (generations G₀–G₂) was revealed by mass spectrometric detection of defective molecules with a net gain of a single carbon atom as compared to expected compounds. Combining MS/MS experiments performed on different electrosprayed precursor ions (protonated molecules and lithiated adducts) with NMR analyses allowed the origin of these by-products to be elucidated. Modification of one ethylenediamine end-group of perfect dendrimers into a cyclic imidazolidine moiety was induced by formaldehyde present at trace level in the methanol solvent used as the synthesis medium. Dendrimers studied here were purposely constructed from a triethanolamine core to make them more flexible, as compared to NH₃- or ethylenediamine-core PAMAM, and hence improve their interaction with DNA. Occurrence of this side-reaction would be favored by the particular flexibility of the dendrimer branches.     

Rapid analysis of nucleotide-activated sugars by high-performance liquid chromatography coupled with diode-array detection, electrospray ionization mass spectrometry and nuclear magnetic resonance

A generally applicable method for HPLC analysis of sugar nucleotides was established. Separation was achieved using ion-pair chromatography on a reversed-phase column. Ion-pair reagents were selected and various parameters optimized with respect to separation of 11 of the most important sugar nucleotides and compatibility with on-line detection by electrospray ionization MS and NMR. The method was applied to the on-line analysis of the GDP-D-mannose-4,6-dehydratase (Gmd) and GDP-4-keto-6-deoxy-D-mannose reductase (Rmd) catalyzed conversion of GDP-D-mannose to GDP-D-rhamnose. By LC-NMR, the intermediate product of the reaction was shown to be a mixture of GDP-4-keto-6-deoxy-D-mannose and GDP-3-keto-6-deoxy-D-mannose. Nucleotide co-factors of enzymatic reactions such as ATP and NADH did not interfere with the analysis of nucleotide-activated sugars.     

Baseline correction combined partial least squares algorithm and its application in on-line Fourier transform infrared quantitative analysis

In order to eliminate the lower order polynomial interferences, a new quantitative calibration algorithm “Baseline Correction Combined Partial Least Squares (BCC-PLS)”, which combines baseline correction and conventional PLS, is proposed. By embedding baseline correction constraints into PLS weights selection, the proposed calibration algorithm overcomes the uncertainty in baseline correction and can meet the requirement of on-line attenuated total reflectance Fourier transform infrared (ATR-FTIR) quantitative analysis. The effectiveness of the algorithm is evaluated by the analysis of glucose and marzipan ATR-FTIR spectra. BCC-PLS algorithm shows improved prediction performance over PLS. The root mean square error of cross-validation (RMSECV) on marzipan spectra for the prediction of the moisture is found to be 0.53%, w/w (range 7–19%). The sugar content is predicted with a RMSECV of 2.04%, w/w (range 33–68%).     

Deuterium nuclear magnetic resonance spectroscopy and stable carbon isotope ratio analysis/mass spectrometry of certain monofloral honeys

Quantitative deuterium nuclear magnetic resonance spectroscopy (NMR) has been used in conjunction with stable carbon isotope ratio analysis/mass spectrometry to refine the detection of sugars that have been added to monofloral honeys. The 13C content of sugars indicates the type of photosynthetic metabolism of the plant that synthesized them; the deuterium content is more characteristic of secondary metabolism and of environmental factors. Consequently, determination of the 13C content of honeys and of proteins extracted from the honeys can be used to detect the addition of C4 plant sugars (cane or corn), but it does not reveal the addition of C3 plant sugars such as beet sugar. Deuterium NMR gives useful information for some monofloral honeys. NMR measurement is performed on ethanol obtained from fermentation of the honey and extracted by distillation. The isotopic composition of the ethanol indicates the nature of the sugars from which it was derived. Various types of monofloral honeys were studied, and the results obtained with commercially available honeys demonstrate the usefulness of isotopic analysis and the need to compile a database of authentic honeys to validate or affirm certain results.     

Gas chromatography/flame ionisation detection mass spectrometry for the detection of endogenous urine metabolites for metabonomic studies and its use as a complementary tool to nuclear magnetic resonance spectroscopy

Metabonomics is a relatively new field of research in which the total pool of metabolites in body fluids or tissues from different patient groups is subjected to comparative analysis. Nuclear magnetic resonance (NMR) spectroscopy is the technology that is currently most widely used for the analysis of these highly complex metabolite mixtures, and hundreds of metabolites can be detected without any upfront separation. We have investigated in this study whether gas chromatography (GC) separation in combination with flame ionisation detection (FID) and mass spectrometry (MS) detection can be used for metabolite profiling from urine. We show that although GC sample preparation is much more involved than for NMR, hundreds of metabolites can reproducibly be detected and analysed by GC. We show that the data quality is sufficiently high--particularly if appropriate baseline correction and time-warping methods are applied--to allow for data comparison by chemometrics methods. A sample set of urines from eleven healthy human volunteers was analysed independently by GC and NMR, and subsequent chemometrics analysis of the two datasets showed some similar features. As judged by NIST database searches of the GC/MS data some of the major metabolites that are detected by NMR are also visible by GC/MS. Since in contrast to NMR every peak in GC corresponds to a single metabolite, the electron ionisation spectra can be used to quickly identify metabolites of interest if their reference spectra are present in a searchable database. In summary, we show that GC is a method that can be used as a complementary tool to NMR for metabolite profiling of urine samples.     

Characterization of an unknown component in Noscapine using liquid chromatography-mass spectrometry and proton nuclear magnetic resonance spectroscopy

Analytical and semipreparative LC methods were used to quantitate and isolate an unknown component (Impurity A) found in samples of bulk Noscapine. This component was also examined by LC-ESI-MS and 1H-NMR. It was concluded that the structure of Impurity A only differed from Noscapine in that it possessed a hydroxyl group at position 21 of the isobenzofuranone moiety.     

Extending the application potential of capillary electrophoresis/frontal analysis for drug-plasma protein studies by combining it with mass spectrometry detection

CE/frontal analysis (CE/FA) is probably one of the most frequently used modes of CE for studying affinity interactions. It is typically performed with classic UV-Vis detection that suffers from low concentration sensitivity. To overcome this limitation, the applicability of CE/FA in combination with ESI-MS detection for the investigation of drug–HSA interactions was demonstrated. The developed new method combines the advantages of CE/FA, such as low sample consumption and no labeling or immobilization of interacting partners, with the benefits of MS detection, such as higher selectivity and sensitivity; moreover, it can be used for molecules lacking a fluorophore or chromophore. The binding parameters of tolbutamide (TL) and glimepiride (GLP), first- and second-generation antidiabetics that differ strongly in their solubility in aqueous solutions, were investigated by this CE/FA-MS method. This method, in contrast to the CE/FA method with the most commonly used UV-Vis detection, is more sensitive; an almost three times lower LOD was reached. The binding parameters of TL and GLP were investigated by this CE/FA-MS method and compared with the literature data. The binding constant value of TL obtained by UV-Vis detection was lower than the value obtained by the method hyphenated with MS detection, which is probably given by the influence of the ESI parameters on the stability of drug–HSA complex. In addition, the ratio of TL and HSA concentrations was divergent in both of the experimental approaches. Finally, it can be concluded that both detection methods have their strengths and weaknesses.     

Correction to Extending the trend vector: The trend matrix and sample-based partial least squares

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Correction to Extending the trend vector: The trend matrix and sample-based partial least squares